Tianle LI

Tianle LI

Final Year Undergraduate Student

Hong Kong University of Science and Technology


My research interests include Natural Language Processing, Graph Neural Network, Data Mining and Multi-module Processing. Currently, I am working with Mengyu Zhou on table understanding especially for numerical reasoning with pretrained model. With the ambition of making contribution to the NLP community, I aim to build a robust and efficient NLP system to tackle with the issues of biasness and unfairness in our society.


  • Natural Language Processing (Biasness, Understanding, Robustness)
  • Scoial Network Mining


  • BSc in Data Science and Technology (DSCT), 2017

    The Hong Kong University of Science and Technology



Tensorflow, Pytorch, Pandas, Spacy, etc.

Other Programming Language

C++, JAVA, SQL, MATLAB, etc.

Cloud Platform




Research Intern

Microsoft Research Lab - Asia

Feb 2021 – Sep 2021 Beijing, China

Supervised by senior researcher Mengyu Zhou

  • Design and implement the advanced algorithm for key phrases extraction. (This work has been deployed in Forms, Teams as Word cloud insight to provide a visualization of responses for text questions. It recieved very positive feedbacks from the customers!)
  • Learning Analysis Semantics over Tabular Data via Conditional Formatting as Proxy. In this research project, we proposed a sophiscated method to encode the table context with good comprehension to numerical tabular data, which can select condition type, formatting type and critical values to automatically recommend conditional formatting visualization for human beings.

We will submit a paper to VLDB in November.


Final Year Project: Fake News Detection on Social Networks


Sep 2020 – Sep 2021 Hong Kong

Under the supervision from Professor Raymond, Wong, we propose a novel Transformer-based model: HetTransformer to solve the fake news detection problem on social networks, which utilizes the structure-aware Transformer and temperal embedding to capture the news propagation patterns in social media. Experiments on three real-world datasets demonstrate that our model is able to outperform the state-of-the-art baselines in fake news detection.

We will submit a paper to WWW in October.


Junior Research Assistant


Jun 2020 – Aug 2020 Hong Kong

We aim at generating adversarial examples in text to attack pretrained BERT model in black box setting under budget constraint (merely query much less number of times towards target model with the same level of success rate and perturbation rate). We employ all the intermediate failure and successful queries to learn words salience rank globally and locally. Responsibilities include:

  • Research topic proposal.
  • Model algorithm design.
  • Data collection & Experiment.
  • Final paper draft.

This work has been submitted to EMNLP in May.


Summer Research Intern

Hong Kong Applied Science and Technology Research Institute (ASTRI)

Jun 2019 – Aug 2019 Hong Kong
Under the supervision from Abel Ze, Yang, we utilized cross-chain blockchain technology and recurrent neural net network to build a cryptocurrency exchange rate prediction system, which obtained recognition from our department head James Zhibin, Lei.

Undergraduate Research Project


Feb 2019 – Aug 2019 Hong Kong
Under the supervision from Prof. Jian-Feng, Cai, I exploited accelerated alternating projections for robust PCA and low-rank Hankel matrix completion to efficiently decompose a Hankel matrix into a low-rank Hankel matrix and a noisy sparse matrix with MATLAB. After the design of the algorithm, I conducted experiments with real data from stock market which leads to 2% improvement on price prediction accuracy.



I worked in a team and implemented a person portrait matting algorithm based on UNet-structure network. We further built a style transfer system on matting person portrait images with utilization of ResNet and CycleGAN to transfer images from realistic style to Simpsons style on GCP.

Top 10

I worked in a group to Scrape reviews from HK.trip.com and classify positive and negative keywords for distinct targeted customers through sentiment analysis and visualize it with word clouds. With the collection of the datasets and basic analysis, we exploited deep-wide learning model to recommend desired hotels for online customers.


I led a team to build a management system for Premiere Performances, which is a NGO, to implemente search function, nodes graph visualization, engagement and likeness of clients in the database. And we won the bid from Premiere Performances with recognition of managers from J.P. Morgan.


  • tliax@connect.ust.hk
  • (852) 95653901
  • HKUST Jockey Club Hall, 3 Tong Yin Ln, Tseung Kwan O, Hong Kong,